Tag Archives: income

Hedging Disney Ahead Of Earnings

If guests have the nerve to die, they wait, like unwanted calories, until they’ve crossed the line and can do so safely off the property. – The Project On Disney, via Snopes Disney: Estimize Versus Value Investor’s Edge With Disney (NYSE: DIS ) reporting earnings after the close, the nearly 1,200 Disney analysts reporting to Estimize collectively predict the company will beat Wall Street’s consensus earnings estimate, as the graph below shows. Click to enlarge The Estimize consensus earnings estimate shown above, $1.46, is 6 cents ahead of the Wall Street consensus of $1.40. Since its analysts include private investors as well as those from independent research shops, buy-side firms, and sell-side firms, Estimize says its estimates tend to be more accurate than those from Wall Street analysts alone. On the bearish side is Seeking Alpha premium author J Mintzmyer, who runs the Seeking Alpha Marketplace service Value Investor’s Edge . In a Pro Research column ( Time To Short Disney ), Mintzmyer argued the stock was “horribly expensive” (in the comments, Mintzmyer clarifies that, while he still finds the stock overvalued, he is no longer short Disney and feels there are better short opportunities available now). Limiting Downside Risk For Disney Longs For Disney longs boosted by the bullish Estimize earnings prediction, but looking to hedge their downside risk over the next several months, we’ll look at a couple of ways of doing so below the refresher on hedging terms. Refresher On Hedging Terms Recall that puts (short for put options) are contracts that give an investor the right to sell a security for a specified price (the strike price) before a specified date (the expiration date). And calls (short for call options) are contracts that give an investor the right to buy a security for a specified price before a specified date. Optimal puts are the ones that will give you the level of protection you want at the lowest cost. A collar is a type of hedge in which you buy a put option for protection, and at the same time, sell a call option, which gives another investor the right to buy the security from you at a higher strike price by the same expiration date. The proceeds from selling the call option can offset at least part of the cost of buying the put option. An optimal collar is a collar that will give you the level of protection you want at the lowest cost while not capping your possible upside by the expiration date of the hedge by more than you specify. In a nutshell, with a collar, you may be able to reduce the cost of hedging in return for giving up some possible upside. Hedging Disney With Optimal Puts We’re going to use Portfolio Armor’s iOS app to find an optimal put and an optimal collar to hedge Disney, but you don’t need the app to do this. You can find optimal puts and collars yourself by using the process we outlined in this article if you’re willing to take the time and do the work. Whether you run the calculations yourself using the process we outlined or use the app, an additional piece of information you’ll need to supply (along with the number of shares you’re looking to hedge) when scanning for an optimal put is your “threshold”, which refers to the maximum decline you are willing to risk. This will vary depending on your risk tolerance. For the purpose of the examples below, we’ve used a threshold of 15%. If you are more risk-averse, you could use a smaller threshold. And if you are less risk-averse, you could use a larger one. All else equal, though, the higher the threshold, the cheaper it will be to hedge. Here are the optimal puts as of Monday’s close to hedge 200 shares of DIS against a greater-than-15% drop by late October. As you can see at the bottom of the screen capture above, the cost of this protection was $424, or 2.01% of position value. A few points about this hedge: To be conservative, the cost was based on the ask price of the put. In practice, you can often buy puts for less (at some price between the bid and ask). The 15% threshold includes this cost, i.e., in the worst-case scenario, your DIS position would be down 12.99%, not including the hedging cost. The threshold is based on the intrinsic value of the puts, so they may provide more protection than promised if the investor exits after the underlying security declines in the near term, when the puts may still have significant time value . Hedging Disney With An Optimal Collar When searching for an optimal collar, you’ll need one more number in addition to your threshold, your “cap,” which refers to the maximum upside you are willing to limit yourself to if the underlying security appreciates significantly. A logical starting point for the cap is your estimate of how the security will perform over the time period of the hedge. For example, if you’re hedging over a five-month period, and you think a security won’t appreciate more than 6% over that time frame, then it might make sense to use 6% as a cap. You don’t think the security is going to do better than that anyway, so you’re willing to sell someone else the right to call it away if it does better than that. We checked Portfolio Armor’s website to get an estimate of Disney’s potential return over the time frame of the hedge. Every trading day, the site runs two screens to avoid riskier investments on every hedgeable security in the U.S., and then ranks the ones that pass by their potential return. Disney didn’t pass the two screens, do the site didn’t calculate a potential return for it. So we looked at Wall Street’s price targets for the stock via Yahoo Finance (pictured below). We usually work with the median target, but in this case, it’s pretty low relative to the price of the stock. The $110.50 12-month price target represents about a 2% potential return between now and late October. On the other hand, the high target of $130 implies a return of about 9.6% over that time frame. By using a cap of 9%, we were able to eliminate the cost of the hedge in this case, so we used that. As of Monday’s close, this was the optimal collar to hedge 200 shares of DIS against a greater-than-15% drop by late October while not capping an investor’s upside at less than 9% by the end of that time period. As you can see in the first part of the optimal collar above, the cost of the put leg was $328, or 1.56% of position value. But if you look at the second part of the collar below, you’ll see the income generated by selling the call leg was a bit higher: $364, or 1.73% of position value. So, the net cost was negative, meaning an investor opening this collar would have collected an amount equal to $36, or -0.17% of position value. Two notes on this hedge: Similar to the situation with the optimal puts, to be conservative, the cost of the optimal collar was calculated using the ask price of the puts and the bid price of the calls. In practice, an investor can often buy puts for less and sell calls for more (again, at some price between the bid and the ask), so in reality, an investor would likely have collected more than $36 when opening this collar. As with the optimal puts above, this hedge may provide more protection than promised if the investor exits after the underlying security declines in the near future, due to time value (for an example of this, see this recent article on hedging Apple (NASDAQ: AAPL ), Hedging Apple ). However, if the underlying security spikes in the near future, time value can have the opposite effect, making it costly to exit the position early (for an example of this, see this article on hedging Facebook (NASDAQ: FB ), Facebook Rewards Cautious Investors Less ). Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Smart Beta And The Portfolio Construction Puzzle

The portfolio puzzle The Rubik’s cube has become a popular metaphor for the marketing teams of ETF providers. With good reason. For each client there’s a portfolio construction puzzle to be solved with building blocks, representing geographies, sectors, asset classes, factors and styles. There has been rapid expansion from providers of ETFs tracking main-market indices, with the largest institutional providers capturing the lion’s share of flows, owing to their ability to deliver on four key ETF governance criteria — consistency, liquidity, transparency and, of course, price. This means that ETFs for main market cap-weighted indices are increasingly commoditized. After all, there doesn’t seem to be anything overly smart about replicating market beta, other than the smartness of saving on fees relative to ‘closet-tracker’ active funds. Traditional cap-weighted index investing is a preference: either out of philosophy or necessity. Innovation Means Smarter? Hence R&D of institutional investors, index providers and ETF manufacturers alike has focused more on “smart beta.” This has triggered a slew of innovation – both superficial and substantive. At a superficial end, age-old alternative weighting strategies (e.g. value indices that screen stocks for low book values, or dividend-weighted indices) have been re-branded as being “smart.” In these cases, for “smart” read “non-market-cap weighted.” In fairness, this rebranding is part of broadening of alternative weighting strategies that are factor-based. More substantively, research programs such as EDHEC-Risk Institute’s Scientific Beta have been instrumental in promoting fresh thinking in the field of both factor-based and risk-based smart beta strategies. Factor-Based Approach As a result, providers are focusing on making building blocks smarter. Instead of relying on the ‘traditional’ factor of market capitalization for index inclusion, smart beta indices (and related ETFs) look at alternative factors: book value, dividend yield, volatility, for example. In that respect, the FTSE Russell 1000 Value Index launched in 1987 is probably the oldest factor index on the block. More recent factor indices are stylistic: Both iShares (Oct-14) and Vanguard (Dec-15) have launched global equity factor ETFs focusing on liquidity, minimum volatility, momentum and value. The sophistication of factor-based index construction will continue to increase with the increase in data availability and computing power. Risk-Based Approach Portfolio strategists meanwhile can apply quantitative rules-based approaches to portfolio construction, creating static or dynamic asset allocation strategies from a growing universe of both cap-weighted and alternatively weighted index tracking funds. These strategies — such as maximum Sharpe, minimum variance, equal risk contribution and maximum deconcentration — offer an alternative to the standard but troubled single period mean variance optimization (MVO) approach. MVO’s limitations The single-period MVO approach remains the traditional bedrock of very long-run investing in normal market conditions where the sequence of returns does not matter. However it runs into difficulty in the short-run when markets are non-normal and sequence of returns matters a lot. So unless you are a large endowment with an infinite time horizon, or perhaps can afford to invest for yourself and your family without ever needing to withdraw any capital, relying entirely on the MVO approach for asset allocation gives false comfort. For cases where there are constraints that challenge the MVO model – due to multiple or limited time horizons, expected capital withdrawals, risk budgets, and unstable risk/return/correlation profiles of asset classes (collectively known as real life) — portfolio construction requires a smarter, more adaptive approach that observes, isolates and captures the reward from shifting risk premia over time. Risk-based portfolio strategies attempt to achieve this and are designed to offer a liquid alternative approach to investing that is uncorrelated with traditional single-period MVO strategies. What’s the Problem to Solve? Whether assessing factor-based ETFs or risk-based ETF strategies, at best these new developments all seem very smart. At worst it’s just a bit different. However, as ETFs get smarter and the strategies that combine them become more sophisticated, there’s a risk that the key question in all of this gets lost in an incomprehensible barrage of Greek. The key question for portfolio managers nonetheless remains the same. What client outcome am I targeting? What client need am I trying to solve? For portfolio strategy, whether using a discretionary manager that relies on judgment, or a systematic rules-based approach that relies on quantitative inputs, the key client considerations remain return objective, time horizon, capacity for loss and diversification across asset classes and/or risk premia. Broadening the Toolkit A portfolio strategy has little meaning without an objective that focuses on client outcomes. Factor-based ETFs and risk-based ETF portfolio strategies offer an alternative or additional set of tools to help deliver on those outcomes, in a way that is systematic, liquid and efficient. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: This article has been prepared for research purposes only.

The Small-Cap "Alpha" Myth

There is a common misconception about “alpha” in the small-cap market within the United States. Many professionals believe that once we step out of the mega-cap world of companies like Google, Wal-Mart, Coca-Cola and Apple where there is an army of analysts digging into the vast amounts of data and pricing stocks accordingly, that there is opportunity in its smaller counterparts given the perceived market inefficiency. The story goes that there are fewer analysts covering these particular companies and, therefore, there is an opportunity to produce superior risk-adjusted returns. Whenever we want to research a particular topic in investing, it is always best to start looking into peer-reviewed academic research. In fact, we published an article all the way back in 2001 that covered this particular topic. Our analysis was based on a research paper entitled “The Small Cap Myth” produced by Richard M. Ennis and Michael D. Sebastian of Ennis Knupp Associates, one of the largest pension consulting firms in the country. Based on a sample of 128 small-cap managers, they concluded that once we adjusted for (1) management fees, (2) improper benchmarking, and (3) survivorship bias within the sample, the average “alpha” fell to virtually zero. Aon Hewitt, another large consulting firm, recently published its own research on the small-cap “alpha” myth in January of this year entitled “The Small-Cap Alpha Myth Revisited.” Based on the eVestment Database of small-cap equity managers, the researchers found that the median performance of these managers was worse for the 10-year period ending June 30, 2015 than the original analysis in 2001. The median performance across all styles in the small-cap market was less than 1% (originally around 4%). Once the researchers adjusted for survivorship bias, back-fill bias, liquidity and transaction costs, which the researchers estimated to be almost 200 basis points, the median results were actually negative. Click to enlarge Similarly, we can compare the average performance of all 479 actively managed small cap funds (as classified by Morningstar) against commercial benchmarks like the Russell 2000 Index and S&P Small Cap 600 Index. If we then add small-cap index funds from Dimensional, Vanguard and iShares, we have a nice comparison chart over the 15-year period ending 12/31/2015. As you can see below, the average actively managed small-cap fund underperformed the Russell 2000 Index by 0.24% per year and the DFA U.S. Small Cap Fund by 2.0% per year, net of fees. These results not only highlight the ” arithmetic of active management ” that Nobel Laureate Bill Sharpe reminds investors of, but also the potential benefits of utilizing a strategy, such as the one offered by DFA, that can better capture the small size premium by designing their own DFA small-cap index that has a smaller weighted average market capitalization than other indexes. Click to enlarge How can different index funds produce significantly different performances if they are all targeting the same asset class? In short, differences in performance come from differences in indexes. For example, the Russell 2000 Index focuses on the bottom 2000 companies in terms of market capitalization in the Russell 3000 Index. DFA, on the other hand, defines its Small-Cap Index as a market-capitalization-weighted index of securities of the smallest US companies whose market capitalization falls in the lowest 8% of the total market capitalization of the eligible market ( see details here ). The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions include non-US companies, REITs, UITs and Investment Companies and companies with the lowest profitability and highest relative price within the small cap universe. Profitability is measured as operating income before depreciation and amortization minus interest expense scaled by book. You can find an even more detailed explanation of the historical composition of their indexes in the footnotes below. It is an important reminder that DFA is not new to the indexing industry. In fact, it is one of the pioneers of understanding and implementing index-based strategies. There is no “right” answer, but DFA’s approach seems to better capture the small-cap premium. It is a delicate balance between maintaining strong diversification, pursuing the small cap premium, and keeping trading costs as low as possible. The chart below displays the historical annualized return and standard deviation for a few DFA and Russell Indexes over the last 37 years. You can see that DFA generates a higher return than Russell by better capturing risk premiums in the stock market. Click to enlarge In its own words, Aon Hewitt summed up belief in the small-cap “alpha” with the following: “The widely held assumption that inefficiencies within the U.S. small- cap equity market should lead to greater opportunity for active management than the large-cap equity market appears to be just as mythical in 2015 as it was in 2001. The growth in actively managed assets within the small-cap space over the past 14 years may be significantly contributing to the lack of inefficiency that many market participants erroneously assume.” We couldn’t agree more. Click to enlarge IFA Painting: The Size Premium Disclosure: I am/we are long DFSTX. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.